Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bandpass Sampling01:17

Bandpass Sampling

292
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
292
Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

1.6K
The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
1.6K
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

675
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
675
Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

3.7K
Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
3.7K
IR Spectrum01:19

IR Spectrum

1.5K
When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
1.5K
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

3.3K
When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
3.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Machine learning-based method for determining regional parameters of the HSS model: a case study of Qingdao, China.

Scientific reports·2026
Same author

Sidelobe Suppression Techniques for Near-Field Multistatic SAR.

Sensors (Basel, Switzerland)·2023
Same author

The value of combined PET/MRI, CT and clinical metabolic parameters in differentiating lung adenocarcinoma from squamous cell carcinoma.

Frontiers in oncology·2022
Same author

Positron Emission Tomography/Magnetic Resonance Imaging Radiomics in Predicting Lung Adenocarcinoma and Squamous Cell Carcinoma.

Frontiers in oncology·2022
Same author

Chloride Transport Behaviour and Service Performance of Cracked Concrete Linings in Coastal Subway Tunnels.

Materials (Basel, Switzerland)·2021
Same author

Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system.

Scientific reports·2021

Related Experiment Video

Updated: Oct 22, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.6K

Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications.

Ruben Moya Torres1, Peter W T Yuen1, Changfeng Yuan1,2

  • 1Department of Electronic Warfare, Cranfield University, Swindon SN6 8LA, UK.

Journal of Imaging
|August 30, 2021
PubMed
Summary

This study introduces a novel spatial spectral mutual information (SSMI) band selection (BS) scheme. The SSMI BS method achieves peak classification accuracy with approximately 20 spectral bands, improving hyperspectral imaging analysis.

Keywords:
Hughes phenomenonaccuracy-dimensionality characteristicsband selectionclassificationcurse of dimensionalityhyperspectral imagingmutual informationspatial spectral band selection

More Related Videos

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.2K
Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals
07:24

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals

Published on: April 14, 2020

17.8K

Related Experiment Videos

Last Updated: Oct 22, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.6K
Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.2K
Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals
07:24

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals

Published on: April 14, 2020

17.8K

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Data Science

Background:

  • Traditional band selection (BS) algorithms often require many spectral bands for maximal accuracy, contradicting the 'curse of dimensionality'.
  • This discrepancy limits the practical application of BS in hyperspectral imaging (HSI).

Purpose of the Study:

  • To introduce a novel spatial spectral mutual information (SSMI) band selection (BS) scheme.
  • To address the 'curse of dimensionality' in HSI classification.
  • To validate the efficiency and accuracy of the proposed SSMI BS scheme.

Main Methods:

  • A spatial feature extraction technique is used as a preprocessing step.
  • Mutual information (MI) of spectral bands is clustered to enhance BS efficiency.
  • The SSMI BS scheme is tested on six diverse HSI datasets.

Main Results:

  • A unique 'bell'-shaped accuracy-dimensionality characteristic peaking at around 20 bands was observed for the first time.
  • The SSMI BS scheme achieved approximately 10% higher classification accuracy than seven state-of-the-art BS methods.
  • Classification accuracy was sensitive to the inclusion or exclusion of single crucial bands, highlighting Hughes' phenomenon.

Conclusions:

  • The SSMI BS scheme effectively resolves the 'curse of dimensionality' in HSI.
  • Efficient band selection is crucial for validating Hughes' phenomenon in HSI data analysis.
  • The proposed method offers significant improvements in HSI classification accuracy.